The Development and Potential Applications of an Automated Method for Detecting and Classifying Continuous Glucose Monitoring Patterns.

IF 3.7 Q2 ENDOCRINOLOGY & METABOLISM
Mansur Shomali, Shiping Liu, Abhimanyu Kumbara, Anand Iyer, Guodong Gordon Gao
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引用次数: 0

Abstract

Introduction: Continuous glucose monitoring (CGM) is emerging as a transformative tool for helping people with diabetes self-manage their glucose and supporting clinicians in effective treatment. Unfortunately, many CGM users, and clinicians, find interpreting the large volume of CGM data to be overwhelming and complex. To address this challenge, an efficient, intelligent method for detecting and classifying discernable patterns in CGM data was desired.

Methods: We developed an automated artificial intelligence (AI)-driven method to detect and classify different discernable CGM patterns which called "CGM events." We trained different models using 60 days of CGM data from 27 individuals with diabetes from a publicly available data set and then evaluated model performance using separate test data from the same group. Each event is classified according to clinical significance based on three parameters: (1) glucose category at or near the beginning of the CGM event; (2) a calculated severity score that encompasses both signal shape and temporal characteristics (e.g., how high the CGM curve goes (measured in mg/dL) and how long it stays above target (as established by published consensus guidelines); and (3) the glucose category at or near the end of the event.

Results: The system accurately detected and classified events from actual CGM data. This was also validated with expert diabetes clinicians.

Conclusions: Advanced pattern recognition methods can be used to detect and classify CGM events of interest and may be used to provide actionable insights and self-management support to CGM users and decision support to the clinicians caring for them.

连续血糖监测模式自动检测和分类方法的开发及潜在应用。
导言:连续血糖监测(CGM)正在成为帮助糖尿病患者自我管理血糖和支持临床医生进行有效治疗的变革性工具。遗憾的是,许多 CGM 用户和临床医生发现,解读大量 CGM 数据是一件令人难以承受的复杂工作。为了应对这一挑战,我们需要一种高效、智能的方法来检测 CGM 数据并对其进行分类:我们开发了一种人工智能(AI)驱动的自动方法,用于检测和分类不同的可识别 CGM 模式(称为 "CGM 事件")。我们使用来自公开数据集的 27 名糖尿病患者的 60 天 CGM 数据训练了不同的模型,然后使用来自同一组的单独测试数据评估了模型的性能。每个事件都根据三个参数的临床意义进行分类:(1) CGM 事件开始时或接近开始时的血糖类别;(2) 计算出的严重程度评分,包括信号形状和时间特征(例如,CGM 曲线上升的高度(以毫克/分升为单位)和超过目标值的时间(根据已发布的共识指南确定);以及 (3) 事件结束时或接近结束时的血糖类别:结果:该系统能从实际 CGM 数据中准确检测出血糖事件并对其进行分类。结论:先进的模式识别方法可用于血糖监测:结论:先进的模式识别方法可用于检测和分类感兴趣的 CGM 事件,并可用于为 CGM 用户提供可操作的见解和自我管理支持,以及为护理他们的临床医生提供决策支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Diabetes Science and Technology
Journal of Diabetes Science and Technology Medicine-Internal Medicine
CiteScore
7.50
自引率
12.00%
发文量
148
期刊介绍: The Journal of Diabetes Science and Technology (JDST) is a bi-monthly, peer-reviewed scientific journal published by the Diabetes Technology Society. JDST covers scientific and clinical aspects of diabetes technology including glucose monitoring, insulin and metabolic peptide delivery, the artificial pancreas, digital health, precision medicine, social media, cybersecurity, software for modeling, physiologic monitoring, technology for managing obesity, and diagnostic tests of glycation. The journal also covers the development and use of mobile applications and wireless communication, as well as bioengineered tools such as MEMS, new biomaterials, and nanotechnology to develop new sensors. Articles in JDST cover both basic research and clinical applications of technologies being developed to help people with diabetes.
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